Microelectromechanical systems (MEMS) are commonly fabricated on silicon (Si) or silicon-on-insulator (SOI) wafers, much as standard integrated circuits are. However, MEMS devices include moving parts on the wafers as well as electrical components. Examples of MEMS devices include gyroscopes, accelerometers, and microphones. MEMS devices can also include actuators that move to apply force on an object. Examples include microrobotic manipulators.
However, when a MEMS device is fabricated, the dimensions of the structures fabricated often do not match the dimensions specified in the layout. This can result from, e.g., under- or over-etching. Such mismatches can change the performance of the devices away from the intended performance. Due to variations from causes such as fabrication processing, packaging, actuation signals, and external disturbances, the true performance can be greater than 100% different than performance predicted based on the original design. It is desirable to reduce this variation to increase yield of MEMS devices.
Some attempts have been made to electronically tune microstructures by various forms of feedback control. These include position controlled feedback to control the effective stiffness of a micro-cantilever to improve the quality factor Q for biological sensing applications; electrostatic force-feedback to improve linearity, bandwidth, and dynamic range; digital force-feedback to a MEMS gyroscope in order to lower the noise floor down to the thermal noise limit; and frequency tuning of micro-resonators using a combination of Joule heating and electrostatic force. Micro-resonators with tapered comb fingers for electrostatic, post-fabrication frequency tuning have also been described. Electrostatic capacitor sensor and actuator pairs have been used to sense a displacement, and force feedback pulses for have been used for position re-zeroing. Velocity feedback control has been described to control damping. Time varying stiffness has been described for parametric amplification. There is a continuing need for ways of reducing variation or improving performance by feedback control.
Moreover, various attempts have been made to adjust or analyze the design of MEMS structures. Examples include those using a statistical framework for quantifying uncertainties through probability densities. However, quantifying uncertainties does not necessarily permit reducing those uncertainties. Monte Carlo (MC) algorithms have been used, for example, to consider the uncertainties associated with MEMS modeling parameters. One prior scheme used a robust optimization approach based on MC to estimate the worst case during the design of a micro gyroscope. A genetic algorithm based on MC simulation has been used for optimizing the filter performance of a MEMS resonator in terms of the shape of the frequency response curve. Probabilistic design systems such as in the ANSYS software have been used to study the effect of various geometrical features on the design of a comb drive. However, MC algorithms can have high or impractical computational costs. Stochastic approaches have also been used to model uncertainties in the input parameters of micromechanical devices and to quantify their effect on the final performance of the device. However, quantifying is not reducing, as noted above. There is, therefore, a continuing need of ways of improving the performance of MEMS devices, whether by dynamic tuning or by design adjustment.
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The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.